Dictionary-Based interpolation technique for text quality enhancement

Figures & data

Figure 1. An example of up-scaling using interpolation, wherein the resolution of an input image is increased by a factor of two.

Figure 2. Process of the proposed dictionary-based interpolation algorithm.

Figure 3. An example of extracting the highest majority using majority recognition.

Figure 4. Encoding and decoding in the proposed method for the current block in Figure 3: (a) the input code and the image with a low resolution, (b) the code dictionary, and (c) the output code and the image with a high resolution.

Figure 5. Comparison of the subjective qualities of the sample test images of English (top) and Japanese (bottom) text using (a) method 1, (b) method 2, (c) method 3, (d) method 4, and (e) the proposed method.

Figure 6. Blur indexes of the proposed and benchmark methods for the sample test images of: (a) English text, (b) Chinese text, (c) Japanese text, and (d) Korean text.

Table I. Blur indexes of the proposed and benchmark methods.

		Blur Index / Blur Index Difference
Test image		Method 1		Method 2		Method 3		Method 4		Proposed
Image Sequence (# of frames)	German	0.055	0.00	0.053	0.00	0.099	−0.05	0.151	−0.10	0.050
	Chinese	0.084	−0.02	0.080	−0.02	0.130	−0.07	0.175	−0.11	0.063
	English	0.059	−0.01	0.057	0.00	0.103	−0.05	0.154	−0.10	0.053
	Japanese	0.079	−0.02	0.076	−0.02	0.120	−0.06	0.165	−0.11	0.060
	Korean	0.081	−0.02	0.079	−0.02	0.120	−0.06	0.163	−0.10	0.063

Table II. Computation times of the proposed and benchmark methods.

Computation Time (ms)
Method 1	Method 2	Method 3	Method 4	Proposed
19.50	8.60	42.50	3.94	4.73